Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations10000
Missing cells15
Missing cells (%)< 0.1%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory7.8 MiB
Average record size in memory821.2 B

Variable types

Text10
Numeric5
Categorical1

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
Popularity is highly overall correlated with Ranked and 3 other fieldsHigh correlation
Ranked is highly overall correlated with Popularity and 1 other fieldsHigh correlation
Score is highly overall correlated with Popularity and 1 other fieldsHigh correlation
Total Review is highly overall correlated with Popularity and 1 other fieldsHigh correlation
Vote is highly overall correlated with Popularity and 1 other fieldsHigh correlation
Status is highly imbalanced (58.8%) Imbalance
Ranked is uniformly distributed Uniform
Total Review has 3351 (33.5%) zeros Zeros

Reproduction

Analysis started2025-03-02 05:15:35.028647
Analysis finished2025-03-02 05:15:41.201218
Duration6.17 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Title
Text

Distinct9684
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size903.7 KiB
2025-03-02T12:15:41.425116image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length329
Median length182
Mean length32.8157
Min length1

Characters and Unicode

Total characters328157
Distinct characters152
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9374 ?
Unique (%)93.7%

Sample

1st rowBerserk
2nd rowJoJo no Kimyou na Bouken Part 7: Steel Ball Run
3rd rowVagabond
4th rowOne Piece
5th rowMonster
ValueCountFrequency (%)
no 2906
 
5.4%
the 1693
 
3.2%
to 922
 
1.7%
of 787
 
1.5%
ni 703
 
1.3%
wa 609
 
1.1%
a 495
 
0.9%
ga 418
 
0.8%
wo 410
 
0.8%
376
 
0.7%
Other values (13211) 44269
82.6%
2025-03-02T12:15:41.891117image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
43636
 
13.3%
a 26362
 
8.0%
o 24188
 
7.4%
i 22700
 
6.9%
e 22484
 
6.9%
n 19049
 
5.8%
u 14785
 
4.5%
r 12885
 
3.9%
t 12693
 
3.9%
s 11768
 
3.6%
Other values (142) 117607
35.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 328157
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
43636
 
13.3%
a 26362
 
8.0%
o 24188
 
7.4%
i 22700
 
6.9%
e 22484
 
6.9%
n 19049
 
5.8%
u 14785
 
4.5%
r 12885
 
3.9%
t 12693
 
3.9%
s 11768
 
3.6%
Other values (142) 117607
35.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 328157
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
43636
 
13.3%
a 26362
 
8.0%
o 24188
 
7.4%
i 22700
 
6.9%
e 22484
 
6.9%
n 19049
 
5.8%
u 14785
 
4.5%
r 12885
 
3.9%
t 12693
 
3.9%
s 11768
 
3.6%
Other values (142) 117607
35.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 328157
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
43636
 
13.3%
a 26362
 
8.0%
o 24188
 
7.4%
i 22700
 
6.9%
e 22484
 
6.9%
n 19049
 
5.8%
u 14785
 
4.5%
r 12885
 
3.9%
t 12693
 
3.9%
s 11768
 
3.6%
Other values (142) 117607
35.8%

Score
Real number (ℝ)

High correlation 

Distinct213
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.367618
Minimum6.91
Maximum9.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-03-02T12:15:42.076830image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum6.91
5-th percentile6.94
Q17.08
median7.28
Q37.56
95-th percentile8.1405
Maximum9.47
Range2.56
Interquartile range (IQR)0.48

Descriptive statistics

Standard deviation0.37803264
Coefficient of variation (CV)0.051310022
Kurtosis1.6928648
Mean7.367618
Median Absolute Deviation (MAD)0.23
Skewness1.2771877
Sum73676.18
Variance0.14290868
MonotonicityNot monotonic
2025-03-02T12:15:42.252867image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.06 165
 
1.7%
6.97 158
 
1.6%
7.03 157
 
1.6%
7.04 157
 
1.6%
6.92 152
 
1.5%
7.01 151
 
1.5%
7.02 148
 
1.5%
7.05 148
 
1.5%
7.21 147
 
1.5%
7.11 146
 
1.5%
Other values (203) 8471
84.7%
ValueCountFrequency (%)
6.91 146
1.5%
6.92 152
1.5%
6.93 140
1.4%
6.94 145
1.5%
6.95 135
1.4%
6.96 133
1.3%
6.97 158
1.6%
6.98 120
1.2%
6.99 126
1.3%
7 136
1.4%
ValueCountFrequency (%)
9.47 1
< 0.1%
9.3 1
< 0.1%
9.24 1
< 0.1%
9.22 1
< 0.1%
9.15 1
< 0.1%
9.09 1
< 0.1%
9.06 1
< 0.1%
9.03 2
< 0.1%
9.01 1
< 0.1%
9 1
< 0.1%

Vote
Real number (ℝ)

High correlation 

Distinct3986
Distinct (%)39.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3482.5646
Minimum101
Maximum400404
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-03-02T12:15:42.421717image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile134
Q1316
median786.5
Q32232.25
95-th percentile11608.85
Maximum400404
Range400303
Interquartile range (IQR)1916.25

Descriptive statistics

Standard deviation13939.756
Coefficient of variation (CV)4.0027273
Kurtosis282.90335
Mean3482.5646
Median Absolute Deviation (MAD)581.5
Skewness14.256572
Sum34825646
Variance1.9431681 × 108
MonotonicityNot monotonic
2025-03-02T12:15:42.594175image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142 25
 
0.2%
130 25
 
0.2%
185 25
 
0.2%
111 24
 
0.2%
108 23
 
0.2%
140 22
 
0.2%
200 21
 
0.2%
115 21
 
0.2%
131 21
 
0.2%
143 20
 
0.2%
Other values (3976) 9773
97.7%
ValueCountFrequency (%)
101 2
 
< 0.1%
102 7
 
0.1%
103 12
0.1%
104 11
0.1%
105 15
0.1%
106 11
0.1%
107 10
0.1%
108 23
0.2%
109 19
0.2%
110 18
0.2%
ValueCountFrequency (%)
400404 1
< 0.1%
366668 1
< 0.1%
361824 1
< 0.1%
331288 1
< 0.1%
269005 1
< 0.1%
268037 1
< 0.1%
266501 1
< 0.1%
261365 1
< 0.1%
230997 1
< 0.1%
230483 1
< 0.1%

Ranked
Real number (ℝ)

High correlation  Uniform 

Distinct9988
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5000.901
Minimum1
Maximum10004
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-03-02T12:15:42.777418image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile500.95
Q12500.75
median5000.5
Q37501.25
95-th percentile9501.05
Maximum10004
Range10003
Interquartile range (IQR)5000.5

Descriptive statistics

Standard deviation2887.3571
Coefficient of variation (CV)0.57736738
Kurtosis-1.2000036
Mean5000.901
Median Absolute Deviation (MAD)2500.5
Skewness0.00020343047
Sum50009010
Variance8336831
MonotonicityNot monotonic
2025-03-02T12:15:42.949130image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3952 2
 
< 0.1%
3759 2
 
< 0.1%
6013 2
 
< 0.1%
3950 2
 
< 0.1%
1510 2
 
< 0.1%
3975 2
 
< 0.1%
3773 2
 
< 0.1%
3768 2
 
< 0.1%
3962 2
 
< 0.1%
1518 2
 
< 0.1%
Other values (9978) 9980
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
10004 1
< 0.1%
10003 1
< 0.1%
10002 1
< 0.1%
10001 1
< 0.1%
10000 1
< 0.1%
9999 1
< 0.1%
9998 1
< 0.1%
9997 1
< 0.1%
9996 1
< 0.1%
9995 1
< 0.1%

Popularity
Real number (ℝ)

High correlation 

Distinct9384
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8707.6479
Minimum1
Maximum33358
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-03-02T12:15:43.121328image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile526.95
Q13099.75
median7346
Q313222
95-th percentile21186.1
Maximum33358
Range33357
Interquartile range (IQR)10122.25

Descriptive statistics

Standard deviation6663.2588
Coefficient of variation (CV)0.76521913
Kurtosis-0.16004457
Mean8707.6479
Median Absolute Deviation (MAD)4805.5
Skewness0.74897795
Sum87076479
Variance44399018
MonotonicityNot monotonic
2025-03-02T12:15:43.375470image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9630 3
 
< 0.1%
10761 3
 
< 0.1%
8530 3
 
< 0.1%
15828 3
 
< 0.1%
12168 3
 
< 0.1%
9467 3
 
< 0.1%
15998 3
 
< 0.1%
19427 3
 
< 0.1%
9111 3
 
< 0.1%
4923 3
 
< 0.1%
Other values (9374) 9970
99.7%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
33358 1
< 0.1%
32693 1
< 0.1%
32621 1
< 0.1%
32567 1
< 0.1%
31988 1
< 0.1%
31764 1
< 0.1%
31612 1
< 0.1%
31225 1
< 0.1%
31191 1
< 0.1%
31091 1
< 0.1%
Distinct6066
Distinct (%)60.7%
Missing0
Missing (%)0.0%
Memory size603.2 KiB
2025-03-02T12:15:43.667634image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.7529
Min length3

Characters and Unicode

Total characters47529
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3961 ?
Unique (%)39.6%

Sample

1st row665,300
2nd row256,146
3rd row364,891
4th row599,278
5th row236,355
ValueCountFrequency (%)
1,279 9
 
0.1%
1,720 9
 
0.1%
954 9
 
0.1%
710 9
 
0.1%
697 9
 
0.1%
1,524 8
 
0.1%
831 8
 
0.1%
859 8
 
0.1%
652 8
 
0.1%
774 8
 
0.1%
Other values (6056) 9915
99.2%
2025-03-02T12:15:44.130201image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 7887
16.6%
1 6200
13.0%
2 4807
10.1%
3 4091
8.6%
4 3895
8.2%
5 3679
7.7%
6 3619
7.6%
7 3445
7.2%
8 3382
7.1%
9 3312
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47529
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 7887
16.6%
1 6200
13.0%
2 4807
10.1%
3 4091
8.6%
4 3895
8.2%
5 3679
7.7%
6 3619
7.6%
7 3445
7.2%
8 3382
7.1%
9 3312
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47529
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 7887
16.6%
1 6200
13.0%
2 4807
10.1%
3 4091
8.6%
4 3895
8.2%
5 3679
7.7%
6 3619
7.6%
7 3445
7.2%
8 3382
7.1%
9 3312
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47529
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 7887
16.6%
1 6200
13.0%
2 4807
10.1%
3 4091
8.6%
4 3895
8.2%
5 3679
7.7%
6 3619
7.6%
7 3445
7.2%
8 3382
7.1%
9 3312
7.0%
Distinct1121
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Memory size575.9 KiB
2025-03-02T12:15:44.433865image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length7
Median length6
Mean length1.9621
Min length1

Characters and Unicode

Total characters19621
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique673 ?
Unique (%)6.7%

Sample

1st row122,841
2nd row42,864
3rd row40,158
4th row114,531
5th row20,501
ValueCountFrequency (%)
3 458
 
4.6%
2 445
 
4.5%
1 415
 
4.2%
4 401
 
4.0%
5 379
 
3.8%
7 322
 
3.2%
6 311
 
3.1%
0 289
 
2.9%
8 251
 
2.5%
9 225
 
2.2%
Other values (1111) 6504
65.0%
2025-03-02T12:15:44.873214image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4001
20.4%
2 2747
14.0%
3 2260
11.5%
4 1877
9.6%
5 1665
8.5%
0 1427
 
7.3%
6 1402
 
7.1%
7 1392
 
7.1%
8 1235
 
6.3%
9 1141
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19621
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4001
20.4%
2 2747
14.0%
3 2260
11.5%
4 1877
9.6%
5 1665
8.5%
0 1427
 
7.3%
6 1402
 
7.1%
7 1392
 
7.1%
8 1235
 
6.3%
9 1141
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19621
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4001
20.4%
2 2747
14.0%
3 2260
11.5%
4 1877
9.6%
5 1665
8.5%
0 1427
 
7.3%
6 1402
 
7.1%
7 1392
 
7.1%
8 1235
 
6.3%
9 1141
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19621
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4001
20.4%
2 2747
14.0%
3 2260
11.5%
4 1877
9.6%
5 1665
8.5%
0 1427
 
7.3%
6 1402
 
7.1%
7 1392
 
7.1%
8 1235
 
6.3%
9 1141
 
5.8%
Distinct68
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size583.6 KiB
2025-03-02T12:15:45.028317image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length7
Median length1
Mean length2.7491
Min length1

Characters and Unicode

Total characters27491
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.1%

Sample

1st rowUnknown
2nd row24
3rd row37
4th rowUnknown
5th row18
ValueCountFrequency (%)
unknown 2632
26.3%
1 1795
17.9%
2 852
 
8.5%
3 823
 
8.2%
4 601
 
6.0%
5 490
 
4.9%
6 344
 
3.4%
7 311
 
3.1%
10 272
 
2.7%
8 257
 
2.6%
Other values (58) 1623
16.2%
2025-03-02T12:15:45.340547image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 7896
28.7%
1 3261
11.9%
U 2632
 
9.6%
k 2632
 
9.6%
o 2632
 
9.6%
w 2632
 
9.6%
2 1402
 
5.1%
3 1085
 
3.9%
4 782
 
2.8%
5 644
 
2.3%
Other values (5) 1893
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27491
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 7896
28.7%
1 3261
11.9%
U 2632
 
9.6%
k 2632
 
9.6%
o 2632
 
9.6%
w 2632
 
9.6%
2 1402
 
5.1%
3 1085
 
3.9%
4 782
 
2.8%
5 644
 
2.3%
Other values (5) 1893
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27491
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 7896
28.7%
1 3261
11.9%
U 2632
 
9.6%
k 2632
 
9.6%
o 2632
 
9.6%
w 2632
 
9.6%
2 1402
 
5.1%
3 1085
 
3.9%
4 782
 
2.8%
5 644
 
2.3%
Other values (5) 1893
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27491
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 7896
28.7%
1 3261
11.9%
U 2632
 
9.6%
k 2632
 
9.6%
o 2632
 
9.6%
w 2632
 
9.6%
2 1402
 
5.1%
3 1085
 
3.9%
4 782
 
2.8%
5 644
 
2.3%
Other values (5) 1893
 
6.9%
Distinct380
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size587.6 KiB
2025-03-02T12:15:45.601543image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length7
Median length4
Mean length3.1549
Min length1

Characters and Unicode

Total characters31549
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique120 ?
Unique (%)1.2%

Sample

1st rowUnknown
2nd row96
3rd row327
4th rowUnknown
5th row162
ValueCountFrequency (%)
unknown 2533
25.3%
5 392
 
3.9%
1 355
 
3.5%
6 324
 
3.2%
4 287
 
2.9%
8 191
 
1.9%
7 183
 
1.8%
12 171
 
1.7%
10 159
 
1.6%
11 145
 
1.5%
Other values (370) 5260
52.6%
2025-03-02T12:15:46.011341image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 7599
24.1%
1 2963
 
9.4%
U 2533
 
8.0%
k 2533
 
8.0%
o 2533
 
8.0%
w 2533
 
8.0%
2 1807
 
5.7%
4 1440
 
4.6%
3 1438
 
4.6%
5 1402
 
4.4%
Other values (5) 4768
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31549
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 7599
24.1%
1 2963
 
9.4%
U 2533
 
8.0%
k 2533
 
8.0%
o 2533
 
8.0%
w 2533
 
8.0%
2 1807
 
5.7%
4 1440
 
4.6%
3 1438
 
4.6%
5 1402
 
4.4%
Other values (5) 4768
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31549
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 7599
24.1%
1 2963
 
9.4%
U 2533
 
8.0%
k 2533
 
8.0%
o 2533
 
8.0%
w 2533
 
8.0%
2 1807
 
5.7%
4 1440
 
4.6%
3 1438
 
4.6%
5 1402
 
4.4%
Other values (5) 4768
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31549
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 7599
24.1%
1 2963
 
9.4%
U 2533
 
8.0%
k 2533
 
8.0%
o 2533
 
8.0%
w 2533
 
8.0%
2 1807
 
5.7%
4 1440
 
4.6%
3 1438
 
4.6%
5 1402
 
4.4%
Other values (5) 4768
15.1%

Status
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size639.0 KiB
Finished
7880 
Publishing
1993 
On Hiatus
 
105
Discontinued
 
22

Length

Max length12
Median length8
Mean length8.4179
Min length8

Characters and Unicode

Total characters84179
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPublishing
2nd rowFinished
3rd rowOn Hiatus
4th rowPublishing
5th rowFinished

Common Values

ValueCountFrequency (%)
Finished 7880
78.8%
Publishing 1993
 
19.9%
On Hiatus 105
 
1.1%
Discontinued 22
 
0.2%

Length

2025-03-02T12:15:46.183338image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T12:15:46.324051image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
finished 7880
78.0%
publishing 1993
 
19.7%
on 105
 
1.0%
hiatus 105
 
1.0%
discontinued 22
 
0.2%

Most occurring characters

ValueCountFrequency (%)
i 19895
23.6%
n 10022
11.9%
s 10000
11.9%
h 9873
11.7%
e 7902
 
9.4%
d 7902
 
9.4%
F 7880
 
9.4%
u 2120
 
2.5%
g 1993
 
2.4%
l 1993
 
2.4%
Other values (10) 4599
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84179
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 19895
23.6%
n 10022
11.9%
s 10000
11.9%
h 9873
11.7%
e 7902
 
9.4%
d 7902
 
9.4%
F 7880
 
9.4%
u 2120
 
2.5%
g 1993
 
2.4%
l 1993
 
2.4%
Other values (10) 4599
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84179
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 19895
23.6%
n 10022
11.9%
s 10000
11.9%
h 9873
11.7%
e 7902
 
9.4%
d 7902
 
9.4%
F 7880
 
9.4%
u 2120
 
2.5%
g 1993
 
2.4%
l 1993
 
2.4%
Other values (10) 4599
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84179
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 19895
23.6%
n 10022
11.9%
s 10000
11.9%
h 9873
11.7%
e 7902
 
9.4%
d 7902
 
9.4%
F 7880
 
9.4%
u 2120
 
2.5%
g 1993
 
2.4%
l 1993
 
2.4%
Other values (10) 4599
 
5.5%
Distinct8486
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
2025-03-02T12:15:46.496258image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length30
Median length29
Mean length22.9816
Min length4

Characters and Unicode

Total characters229816
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7866 ?
Unique (%)78.7%

Sample

1st rowAug 25, 1989 to ?
2nd rowJan 19, 2004 to Apr 19, 2011
3rd rowSep 3, 1998 to May 21, 2015
4th rowJul 22, 1997 to ?
5th rowDec 5, 1994 to Dec 20, 2001
ValueCountFrequency (%)
to 8678
 
15.5%
2225
 
4.0%
dec 1366
 
2.4%
apr 1318
 
2.4%
aug 1312
 
2.3%
jul 1291
 
2.3%
oct 1248
 
2.2%
jun 1232
 
2.2%
nov 1182
 
2.1%
mar 1177
 
2.1%
Other values (109) 35004
62.5%
2025-03-02T12:15:46.834475image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
61234
26.6%
2 25912
11.3%
0 23140
 
10.1%
1 16370
 
7.1%
, 13906
 
6.1%
t 10049
 
4.4%
o 9983
 
4.3%
9 5459
 
2.4%
u 3835
 
1.7%
a 3797
 
1.7%
Other values (26) 56131
24.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 229816
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
61234
26.6%
2 25912
11.3%
0 23140
 
10.1%
1 16370
 
7.1%
, 13906
 
6.1%
t 10049
 
4.4%
o 9983
 
4.3%
9 5459
 
2.4%
u 3835
 
1.7%
a 3797
 
1.7%
Other values (26) 56131
24.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 229816
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
61234
26.6%
2 25912
11.3%
0 23140
 
10.1%
1 16370
 
7.1%
, 13906
 
6.1%
t 10049
 
4.4%
o 9983
 
4.3%
9 5459
 
2.4%
u 3835
 
1.7%
a 3797
 
1.7%
Other values (26) 56131
24.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 229816
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
61234
26.6%
2 25912
11.3%
0 23140
 
10.1%
1 16370
 
7.1%
, 13906
 
6.1%
t 10049
 
4.4%
o 9983
 
4.3%
9 5459
 
2.4%
u 3835
 
1.7%
a 3797
 
1.7%
Other values (26) 56131
24.4%

Genres
Text

Distinct917
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Memory size784.6 KiB
2025-03-02T12:15:46.988857image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length103
Median length86
Mean length23.3329
Min length2

Characters and Unicode

Total characters233329
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique416 ?
Unique (%)4.2%

Sample

1st row['Action', 'Adventure', 'Award Winning', 'Drama', 'Fantasy', 'Horror', 'Supernatural']
2nd row['Action', 'Adventure', 'Mystery', 'Supernatural']
3rd row['Action', 'Adventure', 'Award Winning']
4th row['Action', 'Adventure', 'Fantasy']
5th row['Award Winning', 'Drama', 'Mystery']
ValueCountFrequency (%)
romance 3090
12.0%
comedy 2944
11.4%
2714
10.5%
drama 2360
9.2%
fantasy 2277
8.8%
action 2100
 
8.2%
supernatural 1572
 
6.1%
adventure 1090
 
4.2%
slice 1070
 
4.2%
of 1070
 
4.2%
Other values (15) 5467
21.2%
2025-03-02T12:15:47.326247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 40022
17.2%
a 15813
 
6.8%
15754
 
6.8%
e 13509
 
5.8%
, 12725
 
5.5%
o 11166
 
4.8%
n 11152
 
4.8%
[ 10000
 
4.3%
] 10000
 
4.3%
r 9150
 
3.9%
Other values (29) 84038
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 233329
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 40022
17.2%
a 15813
 
6.8%
15754
 
6.8%
e 13509
 
5.8%
, 12725
 
5.5%
o 11166
 
4.8%
n 11152
 
4.8%
[ 10000
 
4.3%
] 10000
 
4.3%
r 9150
 
3.9%
Other values (29) 84038
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 233329
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 40022
17.2%
a 15813
 
6.8%
15754
 
6.8%
e 13509
 
5.8%
, 12725
 
5.5%
o 11166
 
4.8%
n 11152
 
4.8%
[ 10000
 
4.3%
] 10000
 
4.3%
r 9150
 
3.9%
Other values (29) 84038
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 233329
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 40022
17.2%
a 15813
 
6.8%
15754
 
6.8%
e 13509
 
5.8%
, 12725
 
5.5%
o 11166
 
4.8%
n 11152
 
4.8%
[ 10000
 
4.3%
] 10000
 
4.3%
r 9150
 
3.9%
Other values (29) 84038
36.0%

Themes
Text

Distinct534
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size623.5 KiB
2025-03-02T12:15:47.483487image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length73
Median length2
Mean length6.8368
Min length2

Characters and Unicode

Total characters68368
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique316 ?
Unique (%)3.2%

Sample

1st row['Gore', 'Military', 'Mythology', 'Psychological']
2nd row[]
3rd row['Historical', 'Samurai']
4th row[]
5th row['Adult Cast', 'Psychological']
ValueCountFrequency (%)
8220
62.0%
school 825
 
6.2%
isekai 249
 
1.9%
psychological 238
 
1.8%
harem 227
 
1.7%
historical 213
 
1.6%
reincarnation 186
 
1.4%
arts 176
 
1.3%
martial 120
 
0.9%
military 119
 
0.9%
Other values (59) 2686
 
20.3%
2025-03-02T12:15:47.834813image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
[ 10000
14.6%
] 10000
14.6%
' 8046
 
11.8%
o 3659
 
5.4%
3259
 
4.8%
a 2994
 
4.4%
i 2718
 
4.0%
l 2507
 
3.7%
e 2423
 
3.5%
, 2243
 
3.3%
Other values (40) 20519
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 68368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
[ 10000
14.6%
] 10000
14.6%
' 8046
 
11.8%
o 3659
 
5.4%
3259
 
4.8%
a 2994
 
4.4%
i 2718
 
4.0%
l 2507
 
3.7%
e 2423
 
3.5%
, 2243
 
3.3%
Other values (40) 20519
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 68368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
[ 10000
14.6%
] 10000
14.6%
' 8046
 
11.8%
o 3659
 
5.4%
3259
 
4.8%
a 2994
 
4.4%
i 2718
 
4.0%
l 2507
 
3.7%
e 2423
 
3.5%
, 2243
 
3.3%
Other values (40) 20519
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 68368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
[ 10000
14.6%
] 10000
14.6%
' 8046
 
11.8%
o 3659
 
5.4%
3259
 
4.8%
a 2994
 
4.4%
i 2718
 
4.0%
l 2507
 
3.7%
e 2423
 
3.5%
, 2243
 
3.3%
Other values (40) 20519
30.0%

Author
Text

Distinct6300
Distinct (%)63.1%
Missing15
Missing (%)0.1%
Memory size902.7 KiB
2025-03-02T12:15:48.105269image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length1496
Median length777
Mean length34.769254
Min length2

Characters and Unicode

Total characters347171
Distinct characters84
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4767 ?
Unique (%)47.7%

Sample

1st rowMiura, Kentarou (Story & Art), Studio Gaga (Art)
2nd rowAraki, Hirohiko (Story & Art)
3rd rowInoue, Takehiko (Story & Art), Yoshikawa, Eiji (Story)
4th rowOda, Eiichiro (Story & Art)
5th rowUrasawa, Naoki (Story & Art)
ValueCountFrequency (%)
story 11348
19.9%
art 11165
19.6%
7982
 
14.0%
yuu 151
 
0.3%
yuki 119
 
0.2%
lee 115
 
0.2%
kim 102
 
0.2%
akira 100
 
0.2%
yuuki 83
 
0.1%
takahashi 82
 
0.1%
Other values (7214) 25730
45.2%
2025-03-02T12:15:48.558433image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
46992
 
13.5%
r 28347
 
8.2%
t 26990
 
7.8%
o 24930
 
7.2%
a 22758
 
6.6%
i 17751
 
5.1%
, 15872
 
4.6%
( 14532
 
4.2%
) 14532
 
4.2%
S 14429
 
4.2%
Other values (74) 120038
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 347171
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
46992
 
13.5%
r 28347
 
8.2%
t 26990
 
7.8%
o 24930
 
7.2%
a 22758
 
6.6%
i 17751
 
5.1%
, 15872
 
4.6%
( 14532
 
4.2%
) 14532
 
4.2%
S 14429
 
4.2%
Other values (74) 120038
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 347171
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
46992
 
13.5%
r 28347
 
8.2%
t 26990
 
7.8%
o 24930
 
7.2%
a 22758
 
6.6%
i 17751
 
5.1%
, 15872
 
4.6%
( 14532
 
4.2%
) 14532
 
4.2%
S 14429
 
4.2%
Other values (74) 120038
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 347171
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
46992
 
13.5%
r 28347
 
8.2%
t 26990
 
7.8%
o 24930
 
7.2%
a 22758
 
6.6%
i 17751
 
5.1%
, 15872
 
4.6%
( 14532
 
4.2%
) 14532
 
4.2%
S 14429
 
4.2%
Other values (74) 120038
34.6%

Total Review
Real number (ℝ)

High correlation  Zeros 

Distinct108
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7594
Minimum0
Maximum444
Zeros3351
Zeros (%)33.5%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-03-02T12:15:48.750183image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile14
Maximum444
Range444
Interquartile range (IQR)3

Descriptive statistics

Standard deviation11.976404
Coefficient of variation (CV)3.1857222
Kurtosis358.78519
Mean3.7594
Median Absolute Deviation (MAD)1
Skewness14.65933
Sum37594
Variance143.43426
MonotonicityNot monotonic
2025-03-02T12:15:48.920685image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3351
33.5%
1 2300
23.0%
2 1293
 
12.9%
3 786
 
7.9%
4 455
 
4.5%
5 353
 
3.5%
6 245
 
2.5%
7 200
 
2.0%
8 128
 
1.3%
9 105
 
1.1%
Other values (98) 784
 
7.8%
ValueCountFrequency (%)
0 3351
33.5%
1 2300
23.0%
2 1293
 
12.9%
3 786
 
7.9%
4 455
 
4.5%
5 353
 
3.5%
6 245
 
2.5%
7 200
 
2.0%
8 128
 
1.3%
9 105
 
1.1%
ValueCountFrequency (%)
444 1
< 0.1%
355 1
< 0.1%
261 1
< 0.1%
258 1
< 0.1%
238 1
< 0.1%
234 1
< 0.1%
206 1
< 0.1%
180 1
< 0.1%
147 1
< 0.1%
139 1
< 0.1%
Distinct608
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size645.4 KiB
2025-03-02T12:15:49.046686image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length14
Median length9
Mean length9.0712
Min length9

Characters and Unicode

Total characters90712
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique404 ?
Unique (%)4.0%

Sample

1st row[233, 15, 10]
2nd row[120, 7, 1]
3rd row[88, 8, 1]
4th row[173, 17, 16]
5th row[64, 7, 5]
ValueCountFrequency (%)
0 20138
67.1%
1 4770
 
15.9%
2 1898
 
6.3%
3 957
 
3.2%
4 506
 
1.7%
5 375
 
1.2%
6 238
 
0.8%
7 177
 
0.6%
8 130
 
0.4%
9 110
 
0.4%
Other values (79) 701
 
2.3%
2025-03-02T12:15:49.407584image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 20268
22.3%
, 20000
22.0%
20000
22.0%
[ 10000
11.0%
] 10000
11.0%
1 5284
 
5.8%
2 2117
 
2.3%
3 1099
 
1.2%
4 606
 
0.7%
5 451
 
0.5%
Other values (4) 887
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90712
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 20268
22.3%
, 20000
22.0%
20000
22.0%
[ 10000
11.0%
] 10000
11.0%
1 5284
 
5.8%
2 2117
 
2.3%
3 1099
 
1.2%
4 606
 
0.7%
5 451
 
0.5%
Other values (4) 887
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90712
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 20268
22.3%
, 20000
22.0%
20000
22.0%
[ 10000
11.0%
] 10000
11.0%
1 5284
 
5.8%
2 2117
 
2.3%
3 1099
 
1.2%
4 606
 
0.7%
5 451
 
0.5%
Other values (4) 887
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90712
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 20268
22.3%
, 20000
22.0%
20000
22.0%
[ 10000
11.0%
] 10000
11.0%
1 5284
 
5.8%
2 2117
 
2.3%
3 1099
 
1.2%
4 606
 
0.7%
5 451
 
0.5%
Other values (4) 887
 
1.0%

Interactions

2025-03-02T12:15:40.148967image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T12:15:37.623062image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T12:15:38.210060image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T12:15:38.880306image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T12:15:39.456055image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T12:15:40.269969image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-03-02T12:15:38.990852image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T12:15:39.568469image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T12:15:40.395168image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T12:15:37.867004image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T12:15:38.462305image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T12:15:39.118574image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T12:15:39.701170image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T12:15:40.502811image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T12:15:37.978955image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T12:15:38.594305image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T12:15:39.228348image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T12:15:39.897966image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T12:15:40.621031image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T12:15:38.103613image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T12:15:38.764307image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T12:15:39.347432image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T12:15:40.027967image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2025-03-02T12:15:49.515776image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
PopularityRankedScoreStatusTotal ReviewVote
Popularity1.0000.534-0.5340.057-0.741-0.952
Ranked0.5341.000-1.0000.053-0.450-0.475
Score-0.534-1.0001.0000.0620.4500.475
Status0.0570.0530.0621.0000.0000.000
Total Review-0.741-0.4500.4500.0001.0000.739
Vote-0.952-0.4750.4750.0000.7391.000

Missing values

2025-03-02T12:15:40.776732image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-02T12:15:41.060773image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TitleScoreVoteRankedPopularityMembersFavoriteVolumesChaptersStatusPublishedGenresThemesAuthorTotal ReviewType Review
0Berserk9.4733128811665,300122,841UnknownUnknownPublishingAug 25, 1989 to ?['Action', 'Adventure', 'Award Winning', 'Drama', 'Fantasy', 'Horror', 'Supernatural']['Gore', 'Military', 'Mythology', 'Psychological']Miura, Kentarou (Story & Art), Studio Gaga (Art)258[233, 15, 10]
1JoJo no Kimyou na Bouken Part 7: Steel Ball Run9.30156368226256,14642,8642496FinishedJan 19, 2004 to Apr 19, 2011['Action', 'Adventure', 'Mystery', 'Supernatural'][]Araki, Hirohiko (Story & Art)128[120, 7, 1]
2Vagabond9.24136403315364,89140,15837327On HiatusSep 3, 1998 to May 21, 2015['Action', 'Adventure', 'Award Winning']['Historical', 'Samurai']Inoue, Takehiko (Story & Art), Yoshikawa, Eiji (Story)97[88, 8, 1]
3One Piece9.2236666843599,278114,531UnknownUnknownPublishingJul 22, 1997 to ?['Action', 'Adventure', 'Fantasy'][]Oda, Eiichiro (Story & Art)206[173, 17, 16]
4Monster9.1593945529236,35520,50118162FinishedDec 5, 1994 to Dec 20, 2001['Award Winning', 'Drama', 'Mystery']['Adult Cast', 'Psychological']Urasawa, Naoki (Story & Art)76[64, 7, 5]
5Slam Dunk9.0973590656163,87915,51431276FinishedSep 18, 1990 to Jun 4, 1996['Award Winning', 'Sports']['School', 'Team Sports']Inoue, Takehiko (Story & Art)52[50, 1, 1]
6Vinland Saga9.06124743719292,67732,273UnknownUnknownPublishingApr 13, 2005 to ?['Action', 'Adventure', 'Award Winning', 'Drama'][]Yukimura, Makoto (Story & Art)81[62, 9, 10]
7Fullmetal Alchemist9.03155123820288,46729,91427116FinishedJul 12, 2001 to Sep 11, 2010['Action', 'Adventure', 'Award Winning', 'Drama', 'Fantasy'][]Arakawa, Hiromu (Story & Art)56[54, 0, 2]
8Grand Blue (Grand Blue Dreaming)9.0364853948171,62117,110UnknownUnknownPublishingApr 7, 2014 to ?[][]Inoue, Kenji (Story), Yoshioka, Kimitake (Art)49[44, 2, 3]
9Oyasumi Punpun (Goodnight Punpun)9.01174657109426,78650,73213147FinishedMar 15, 2007 to Nov 2, 2013['Drama', 'Slice of Life'][]Asano, Inio (Story & Art)261[205, 31, 25]
TitleScoreVoteRankedPopularityMembersFavoriteVolumesChaptersStatusPublishedGenresThemesAuthorTotal ReviewType Review
9990Hatsujou Junjou☆Douwa6.9115781000464112,893216FinishedMar 26, 2009['Fantasy', 'Romance'][]Hibiki, Ai (Story & Art)3[2, 1, 0]
9991Rebirth Knight6.91632985690831,8787UnknownUnknownPublishingJul 12, 2013 to ?['Action', 'Drama', 'Romance'][]Yu, Jaebeom (Story & Art)0[0, 0, 0]
9992Hyakunen Kesshou Mokuroku (The Hundred-Year Crystal Catalogue)6.914889857129641,159115FinishedJun 20, 2013 to Apr 21, 2014['Adventure', 'Boys Love', 'Fantasy'][]Aoi, Aki (Story & Art)0[0, 0, 0]
9993Yankee-kun na Yamada-kun to Megane-chan to Majo6.911435985858723,18316Unknown1FinishedDec 20, 2013[][]Yoshikawa, Miki (Story & Art)0[0, 0, 0]
9994Ore Alice: Danjo Gyakuten (I Am Alice: Body Swap in Wonderland)6.912969859155018897314FinishedOct 15, 2012 to Nov 15, 2013[][]Kanou, Ayumi (Story & Art)1[1, 0, 0]
9995Mikagura Gakuen Kumikyoku (Mikagura School Suite: The Manga Companion)6.912189860116101,35412631FinishedJul 13, 2013 to Feb 15, 2016['Action', 'Comedy']['School', 'Visual Arts']Sayuki (Art), Last Note. (Story)0[0, 0, 0]
9996Stella to Mille Feuille6.911068986159193,1585312FinishedDec 13, 2013 to Nov 13, 2014[][]Watanabe, Kana (Story & Art)2[0, 2, 0]
9997Stella no Mahou (Magic of Stella)6.91137986214815954910UnknownFinishedAug 18, 2012 to Dec 18, 2021['Comedy', 'Slice of Life']['CGDCT', 'School']cloba.U (Story & Art)0[0, 0, 0]
9998NEET dakedo Hello Work ni Ittara Isekai ni Tsuretekareta6.91302986396881,73914UnknownUnknownPublishingOct 24, 2013 to ?['Action', 'Comedy', 'Fantasy', 'Romance']['Harem', 'Isekai']Sameda, Koban (Art), Katsura, Kasuga (Story)0[0, 0, 0]
9999Floor ni Maou ga Imasu (There's a Demon Lord On the Floor)6.911296986429566,65835UnknownUnknownPublishingOct 27, 2014 to ?['Comedy', 'Fantasy', 'Slice of Life', 'Supernatural', 'Ecchi'][]Kawakami, Masaki (Art), Hato (Story)1[1, 0, 0]

Duplicate rows

Most frequently occurring

TitleScoreVoteRankedPopularityMembersFavoriteVolumesChaptersStatusPublishedGenresThemesAuthorTotal ReviewType Review# duplicates
0Iinazuke Ryokan (Fiancée Inn)7.191110601384282,077313Finished2005['Drama', 'Romance'][]Shimaki, Ako (Story & Art)1[1, 0, 0]2